Update app.py

app.py

@@ -1,18 +1,35 @@
+import torch
+
 import gradio as gr
-import json
-from transformers import AutoTokenizer, AutoModelWithLMHead
-from transformers import pipeline
+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
 tokenizer = AutoTokenizer.from_pretrained("aditi2222/automatic_title_generation")
-model = AutoModelWithLMHead.from_pretrained("aditi2222/automatic_title_generation")
-generator = pipeline(
-  task="text-generation",
-  model=model,
-  tokenizer=tokenizer,
-  framework="tf"
-)
-model = AutoModelWithLMHead.from_pretrained("gpt2")
-def green(prompt):
-    return generator(prompt, max_length=1000)
-    
-iface = gr.Interface(fn=green, inputs=gr.inputs.Textbox(lines=2, placeholder="Put prompt here"), outputs=gr.outputs.Textbox(type="str", label="generated text"))
-iface.launch()
+
+model = AutoModelForSeq2SeqLM.from_pretrained("aditi2222/automatic_title_generation")
+  
+
+def tokenize_data(text):
+    # Tokenize the review body
+    input_ =  str(text) + ' </s>'
+    max_len = 120
+    # tokenize inputs
+    tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
+
+    inputs={"input_ids": tokenized_inputs['input_ids'],
+        "attention_mask": tokenized_inputs['attention_mask']}
+    return inputs
+
+def generate_answers(text):
+    inputs = tokenize_data(text)
+    results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
+                            max_length=120,
+                            top_k=120,
+                            top_p=0.98,
+                            early_stopping=True,
+                            num_return_sequences=1)
+    answer = tokenizer.decode(results[0], skip_special_tokens=True)
+    return answer
+
+iface = gr.Interface(fn=generate_answers, inputs=['text'], outputs=["text"])
+iface.launch(inline=False, share=True)